Youth and Coping in Twelve Countries
Bibliographic record
Abstract
Part I. Introduction 1. Our study and its methodology J. Gibson Cline, C. Baker and M. Dikaiou 2. Concepts, theories and practices M. Dikaiou Part II. The Multinational Study 3. The multinational study: reports from Brazilian, Canadian, Chinese, Filipino, English, Ethiopian immigrant, Greek, Indian, Japanese, Russian, American and Venezuelan young people J. Gibson-Cline Part III. National Studies of Advantaged and Non-advantaged Youth 4. Brazil: university and college students in Campinas E. Pereira 5. Canada: eighteen-year-old youths in their final year of high school L. Martinson, B. Shaw and W. Borgen 6. China: university students in Beijing during the period of reform Chen Qi and Miao Fengchun 7. England: young people in Britain's south and southwest K. Feaviour and David Acres 8. Greece: university and technical institute students in Thessaloniki M. Dikaiou and M. Haritos-Fatouras 9. India: university and technical institute students in Mumbai L. Kashyap 10. Israel: mainstream Jewish youth B. Shafrir 11. Japan: university and business school students in Tokyo K. Honda 12. Russia: university students in Moscow before and after Perestroika N. Talyzina, G. Emelianov and A. Adaskina 13. USA: university students in Pittsburgh, Pennsylvania L. Maccarelli and J. Gibson-Cline 14. Venezuela: university students in Caracas M. Felce-DiPaula Part IV. Special Youth Populations 15. Immigrant youth: Ethiopians in Israel R. Erhard 16. Impoverished youth: samples from Brazil, England and the Philippines E Pereira, K. Feaviour and G. Velazco Part V. Conclusions and Implications for the Helping Professionals 17. Conclusions and implications for theory and practice J. Gibson-Cline
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".